Uncanny Valley 2
“Uncanny Valley 2” is a study that examines adults’ beliefs and feelings about a collection of real-world robots based on a viewing of an 8-second video of that robot.
There are 473 participants.
11 did not enter birthdates. 0 were too young.
| Another gender identity | I prefer not to answer this question | Man | Woman |
|---|---|---|---|
| 3 | 5 | 264 | 190 |
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|
| 20 | 29 | 34 | 36.35 | 42 | 75 |
There are 462 participants after removing anyone under the age of 18.
36 did not complete the study in a sufficient amount of time.
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|
| 0.55 | 3.017 | 4.008 | 5.321 | 5.775 | 50.47 |
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|
| 2.017 | 3.171 | 4.05 | 4.764 | 5.596 | 14.43 |
There are 426 particpants after removing anyone with a study duration outside of the range of 2 - 15 minutes.
| Another gender identity | I prefer not to answer this question | Man | Woman |
|---|---|---|---|
| 3 | 5 | 240 | 178 |
| College graduate | Have not completed high school degree | High school graduate |
|---|---|---|
| 242 | 4 | 127 |
| Postgraduate education |
|---|
| 53 |
Descriptives
Analysis of Questions
Confirmatory Factor Analysis
## lavaan (0.5-23.1097) converged normally after 52 iterations
##
## Number of observations 2079
##
## Estimator ML
## Minimum Function Test Statistic 60.350
## Degrees of freedom 11
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 8629.754
## Degrees of freedom 21
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.994
## Tucker-Lewis Index (TLI) 0.989
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10492.652
## Loglikelihood unrestricted model (H1) -10462.477
##
## Number of free parameters 17
## Akaike (AIC) 21019.304
## Bayesian (BIC) 21115.178
## Sample-size adjusted Bayesian (BIC) 21061.168
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046
## 90 Percent Confidence Interval 0.035 0.058
## P-value RMSEA <= 0.05 0.672
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.021
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## agency =~
## choose 1.000
## think 1.483 0.113 13.076 0.000
## exp =~
## scared 1.000
## pain 1.034 0.020 53.027 0.000
## hungry 0.961 0.019 51.160 0.000
## uv =~
## creepy 1.000
## weird 1.345 0.102 13.244 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## agency ~~
## exp 0.084 0.008 10.044 0.000
## uv 0.070 0.012 5.643 0.000
## exp ~~
## uv 0.053 0.008 6.330 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .choose 0.657 0.029 22.584 0.000
## .think -0.091 0.046 -1.983 0.047
## .scared 0.052 0.002 23.996 0.000
## .pain 0.030 0.002 16.892 0.000
## .hungry 0.036 0.002 20.821 0.000
## .creepy 0.419 0.053 7.973 0.000
## .weird -0.195 0.092 -2.106 0.035
## agency 0.302 0.030 10.065 0.000
## exp 0.137 0.006 23.539 0.000
## uv 0.691 0.060 11.495 0.000## $lambda
## agency exp uv
## choose 0.561 0.000 0.000
## think 1.076 0.000 0.000
## scared 0.000 0.851 0.000
## pain 0.000 0.911 0.000
## hungry 0.000 0.882 0.000
## creepy 0.000 0.000 0.789
## weird 0.000 0.000 1.088
##
## $theta
## choose think scared pain hungry creepy weird
## choose 0.685
## think 0.000 -0.158
## scared 0.000 0.000 0.275
## pain 0.000 0.000 0.000 0.171
## hungry 0.000 0.000 0.000 0.000 0.222
## creepy 0.000 0.000 0.000 0.000 0.000 0.378
## weird 0.000 0.000 0.000 0.000 0.000 0.000 -0.184
##
## $psi
## agency exp uv
## agency 1.000
## exp 0.415 1.000
## uv 0.153 0.172 1.000Partial Correlations
A Priori Variables
##
## Pearson's product-moment correlation
##
## data: RBI$exp.c and RBI$agency.c
## t = 16, df = 2100, p-value <0.0000000000000002
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2931 0.3696
## sample estimates:
## cor
## 0.3319##
## Call:
## lm(formula = uv.c ~ exp.c + agency.c + Gender + Age, data = RBI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.846 -0.746 -0.278 0.529 2.404
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.62674 0.15237 -4.11 0.00004053128 ***
## exp.c 0.10228 0.02284 4.48 0.00000794687 ***
## agency.c 0.13517 0.02259 5.98 0.00000000256 ***
## Gender 0.24439 0.03974 6.15 0.00000000093 ***
## Age -0.00559 0.00203 -2.75 0.0061 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.97 on 2074 degrees of freedom
## Multiple R-squared: 0.0613, Adjusted R-squared: 0.0595
## F-statistic: 33.8 on 4 and 2074 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = uv.c ~ exp.c + agency.c + Gender + Age, data = RBI)
##
## Standardized Coefficients::
## (Intercept) exp.c agency.c Gender Age
## 0.00000 0.10228 0.13517 0.13128 -0.05935K-means clustering of a priori variables
## [1] 347.1| HAHE | HALE | LALE |
|---|---|---|
| 104 | 659 | 1316 |
##
## Call:
## lm(formula = uv ~ cluster.name, data = km)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.510 -0.774 -0.274 0.490 2.226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.5096 0.0968 25.91 < 0.0000000000000002 ***
## cluster.nameHALE -0.4990 0.1042 -4.79 0.00000179874413 ***
## cluster.nameLALE -0.7357 0.1006 -7.31 0.00000000000037 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.988 on 2076 degrees of freedom
## Multiple R-squared: 0.0321, Adjusted R-squared: 0.0312
## F-statistic: 34.5 on 2 and 2076 DF, p-value: 0.00000000000000191##
## Call:
## lm(formula = uv ~ cluster.name, data = km)
##
## Standardized Coefficients::
## (Intercept) cluster.nameHALE cluster.nameLALE
## 0.0000 -0.2315 -0.3535Distribution of robots among k-means clusters
##
## 1 2 3
## atlas 1 0 0
## spot 1 0 0
## festo 0 1 0
## kb 0 1 0
## kf 0 1 0
## nao 0 1 0
## pepper 0 1 0
## tapia 0 1 0
## actroid 0 0 1
## sofia 0 0 1##
## Call:
## lm(formula = uv ~ agency.c + exp.c + robot.group + Gender + Age,
## data = RBI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.368 -0.641 -0.333 0.511 2.570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.19708 0.14687 8.15 0.00000000000000062 ***
## agency.c 0.10475 0.02158 4.85 0.00000129410922935 ***
## exp.c 0.08973 0.02127 4.22 0.00002553597944144 ***
## robot.grouphuman-like 0.74756 0.05953 12.56 < 0.0000000000000002 ***
## robot.grouprobotic -0.18159 0.05083 -3.57 0.00036 ***
## Gender 0.24621 0.03690 6.67 0.00000000003211117 ***
## Age -0.00586 0.00189 -3.10 0.00197 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9 on 2072 degrees of freedom
## Multiple R-squared: 0.197, Adjusted R-squared: 0.195
## F-statistic: 84.8 on 6 and 2072 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = uv ~ agency.c + exp.c + robot.group + Gender + Age,
## data = RBI)
##
## Standardized Coefficients::
## (Intercept) agency.c exp.c
## 0.00000 0.10440 0.08943
## robot.grouphuman-like robot.grouprobotic Gender
## 0.30981 -0.09000 0.13181
## Age
## -0.06202Distribution of participants among k-means clusters
| 1 | 2 | 3 |
|---|---|---|
| 254 | 158 | 9 |
| 0 | HAHE | HALE | LALE |
|---|---|---|---|
| 167 | 11 | 58 | 185 |
| 0 | HALE & HAHE | HALE & LALE | LALE & HAHE |
|---|---|---|---|
| 263 | 5 | 147 | 6 |
Data-driven aggregates
Principal Components Analysis
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## choose 0.07515 0.353921 0.18439 0.21204 0.08988 0.01309 0.005868
## feel 0.16166 0.067631 0.04327 0.13742 0.38383 0.01152 0.052400
## hungry 0.16202 0.105847 0.16455 0.08313 0.07457 0.26062 0.348759
## moral 0.15484 0.008355 0.34667 0.15482 0.11675 0.16914 0.012940
## pain 0.16465 0.095220 0.13301 0.09173 0.13239 0.05776 0.428914
## scared 0.16425 0.097492 0.01472 0.01464 0.09630 0.45947 0.136773
## think 0.11742 0.271536 0.11339 0.30623 0.10628 0.02840 0.014346## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.061 1.127 0.6504 0.5922 0.5313 0.4860 0.4358
## Proportion of Variance 0.607 0.182 0.0604 0.0501 0.0403 0.0337 0.0271
## Cumulative Proportion 0.607 0.788 0.8487 0.8988 0.9391 0.9729 1.0000
##
## Pearson's product-moment correlation
##
## data: pca$PC1 and pca$PC2
## t = 0.00000000000031, df = 2100, p-value = 1
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04299 0.04299
## sample estimates:
## cor
## 0.00000000000000677##
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group + Gender + Age, data = pca)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.360 -0.645 -0.319 0.508 2.565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.20367 0.14705 8.19 0.00000000000000047 ***
## PC1 0.06657 0.00983 6.78 0.00000000001612157 ***
## PC2 0.05771 0.01803 3.20 0.00139 **
## robot.grouphuman-like 0.74069 0.05966 12.42 < 0.0000000000000002 ***
## robot.grouprobotic -0.18386 0.05089 -3.61 0.00031 ***
## Gender 0.24468 0.03693 6.62 0.00000000004418959 ***
## Age -0.00582 0.00190 -3.07 0.00220 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.902 on 2072 degrees of freedom
## Multiple R-squared: 0.195, Adjusted R-squared: 0.193
## F-statistic: 83.6 on 6 and 2072 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group + Gender + Age, data = pca)
##
## Standardized Coefficients::
## (Intercept) PC1 PC2
## 0.00000 0.13673 0.06483
## robot.grouphuman-like robot.grouprobotic Gender
## 0.30697 -0.09112 0.13100
## Age
## -0.06162K-means clustering of a data-driven components
## [1] 1988| HAHE | HALE | LALE |
|---|---|---|
| 114 | 658 | 1307 |
Distribution of robots among k-means clusters (PCA)
##
## 1 2 3
## actroid 1 0 0
## sofia 1 0 0
## festo 0 1 0
## kb 0 1 0
## kf 0 1 0
## nao 0 1 0
## pepper 0 1 0
## tapia 0 1 0
## atlas 0 0 1
## spot 0 0 1##
## Call:
## lm(formula = uv ~ cluster, data = km)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.456 -0.771 -0.271 0.544 2.229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0152 0.0385 52.32 < 0.0000000000000002 ***
## cluster2 0.4409 0.1002 4.40 0.00001143 ***
## cluster3 -0.2443 0.0472 -5.17 0.00000025 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.988 on 2076 degrees of freedom
## Multiple R-squared: 0.0312, Adjusted R-squared: 0.0303
## F-statistic: 33.5 on 2 and 2076 DF, p-value: 0.00000000000000489##
## Call:
## lm(formula = uv ~ cluster, data = km)
##
## Standardized Coefficients::
## (Intercept) cluster2 cluster3
## 0.0000 0.1001 -0.1177Distribution of participants among k-means clusters (PCA)
| 1 | 2 | 3 |
|---|---|---|
| 255 | 156 | 10 |
| 0 | 1 | 2 | 3 |
|---|---|---|---|
| 166 | 58 | 14 | 183 |
| 0 | 1 & 2 | 1 & 3 | 2 & 3 |
|---|---|---|---|
| 265 | 6 | 147 | 3 |
Imputed Data
Analysis of Questions
Exploratory/Confirmatory Factor Analysis
## lavaan (0.5-23.1097) converged normally after 55 iterations
##
## Number of observations 4260
##
## Estimator ML
## Minimum Function Test Statistic 90.078
## Degrees of freedom 11
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 21151.329
## Degrees of freedom 21
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.996
## Tucker-Lewis Index (TLI) 0.993
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17960.535
## Loglikelihood unrestricted model (H1) -17915.496
##
## Number of free parameters 17
## Akaike (AIC) 35955.069
## Bayesian (BIC) 36063.139
## Sample-size adjusted Bayesian (BIC) 36009.120
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.041
## 90 Percent Confidence Interval 0.033 0.049
## P-value RMSEA <= 0.05 0.966
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.020
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## agency =~
## choose 1.000
## think 1.413 0.071 19.800 0.000
## exp =~
## scared 1.000
## pain 1.006 0.011 91.842 0.000
## hungry 0.924 0.010 89.108 0.000
## uv =~
## creepy 1.000
## weird 1.183 0.042 28.221 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## agency ~~
## exp 0.080 0.005 14.994 0.000
## uv 0.088 0.009 9.593 0.000
## exp ~~
## uv 0.057 0.006 10.377 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .choose 0.610 0.019 32.456 0.000
## .think -0.084 0.027 -3.143 0.002
## .scared 0.030 0.001 31.990 0.000
## .pain 0.022 0.001 26.552 0.000
## .hungry 0.023 0.001 30.048 0.000
## .creepy 0.271 0.029 9.470 0.000
## .weird -0.088 0.039 -2.237 0.025
## agency 0.296 0.020 15.017 0.000
## exp 0.124 0.003 37.069 0.000
## uv 0.814 0.036 22.518 0.000## $lambda
## agency exp uv
## choose 0.572 0.000 0.000
## think 1.080 0.000 0.000
## scared 0.000 0.897 0.000
## pain 0.000 0.922 0.000
## hungry 0.000 0.907 0.000
## creepy 0.000 0.000 0.866
## weird 0.000 0.000 1.041
##
## $theta
## choose think scared pain hungry creepy weird
## choose 0.673
## think 0.000 -0.166
## scared 0.000 0.000 0.196
## pain 0.000 0.000 0.000 0.150
## hungry 0.000 0.000 0.000 0.000 0.178
## creepy 0.000 0.000 0.000 0.000 0.000 0.250
## weird 0.000 0.000 0.000 0.000 0.000 0.000 -0.084
##
## $psi
## agency exp uv
## agency 1.000
## exp 0.417 1.000
## uv 0.179 0.181 1.000Partial Correlations
A Priori Variables
##
## Pearson's product-moment correlation
##
## data: RBI.imp$exp.c and RBI.imp$agency.c
## t = 36, df = 4300, p-value <0.0000000000000002
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4617 0.5076
## sample estimates:
## cor
## 0.485##
## Call:
## lm(formula = uv.c ~ exp.c + agency.c + Gender + Age, data = RBI.imp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.088 -0.744 -0.233 0.419 2.504
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.32943 0.18177 -1.81
## exp.c 0.08161 0.01705 4.79
## agency.c 0.16346 0.01699 9.62
## GenderI prefer not to answer this question 0.12543 0.22346 0.56
## GenderMan 0.45487 0.17780 2.56
## GenderWoman 0.66798 0.17819 3.75
## Age -0.00567 0.00142 -3.99
## Pr(>|t|)
## (Intercept) 0.07001 .
## exp.c 0.0000017 ***
## agency.c < 0.0000000000000002 ***
## GenderI prefer not to answer this question 0.57463
## GenderMan 0.01055 *
## GenderWoman 0.00018 ***
## Age 0.0000670 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.966 on 4253 degrees of freedom
## Multiple R-squared: 0.068, Adjusted R-squared: 0.0667
## F-statistic: 51.8 on 6 and 4253 DF, p-value: <0.0000000000000002K-means clustering of a priori variables
## [1] 377.6Distribution of robots among k-means clusters
##
## 1 2 3
## festo 1 0 0
## kb 1 0 0
## kf 1 0 0
## nao 1 0 0
## pepper 1 0 0
## tapia 1 0 0
## actroid 0 1 0
## sofia 0 1 0
## atlas 0 0 1
## spot 0 0 1##
## Call:
## lm(formula = uv ~ cluster, data = km)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.673 -0.762 -0.262 0.327 2.238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7621 0.0190 92.86 <0.0000000000000002 ***
## cluster2 0.2771 0.0326 8.51 <0.0000000000000002 ***
## cluster3 0.9106 0.0791 11.51 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.987 on 4257 degrees of freedom
## Multiple R-squared: 0.0408, Adjusted R-squared: 0.0403
## F-statistic: 90.5 on 2 and 4257 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = uv ~ cluster, data = km)
##
## Standardized Coefficients::
## (Intercept) cluster2 cluster3
## 0.0000 0.1290 0.1744##
## Call:
## lm(formula = uv ~ agency.c + exp.c + robot.group + Gender + Age,
## data = RBI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.666 -0.648 -0.350 0.468 2.565
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.44516 0.17418 8.30
## agency.c 0.12902 0.01637 7.88
## exp.c 0.08757 0.01615 5.42
## robot.grouphuman-like 0.81123 0.04426 18.33
## robot.grouprobotic -0.05352 0.03672 -1.46
## GenderI prefer not to answer this question 0.10925 0.21102 0.52
## GenderMan 0.45009 0.16790 2.68
## GenderWoman 0.66693 0.16827 3.96
## Age -0.00605 0.00134 -4.50
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## agency.c 0.0000000000000041 ***
## exp.c 0.0000000622867409 ***
## robot.grouphuman-like < 0.0000000000000002 ***
## robot.grouprobotic 0.1450
## GenderI prefer not to answer this question 0.6047
## GenderMan 0.0074 **
## GenderWoman 0.0000750464852556 ***
## Age 0.0000068754133205 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.912 on 4251 degrees of freedom
## Multiple R-squared: 0.182, Adjusted R-squared: 0.18
## F-statistic: 118 on 8 and 4251 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = uv ~ agency.c + exp.c + robot.group + Gender + Age,
## data = RBI)
##
## Standardized Coefficients::
## (Intercept)
## 0.00000
## agency.c
## 0.12807
## exp.c
## 0.08692
## robot.grouphuman-like
## 0.32213
## robot.grouprobotic
## -0.02603
## GenderI prefer not to answer this question
## 0.01168
## GenderMan
## 0.22160
## GenderWoman
## 0.32653
## Age
## -0.06338Distribution of participants among k-means clusters
| 1 | 2 | 3 |
|---|---|---|
| 229 | 185 | 12 |
| 0 | 1 | 2 | 3 |
|---|---|---|---|
| 197 | 173 | 49 | 7 |
| 0 | 1 & 2 | 1 & 3 | 2 & 3 |
|---|---|---|---|
| 241 | 173 | 1 | 11 |
Data-driven aggregates
Principal Components Analysis
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## choose 0.0747 0.358496 0.2117172 0.18468 0.08039 0.01209 0.002792
## feel 0.1603 0.059840 0.0001788 0.15381 0.39083 0.01630 0.004330
## hungry 0.1622 0.105589 0.1611379 0.09788 0.07380 0.27867 0.362128
## moral 0.1551 0.003599 0.3181355 0.17725 0.18024 0.12411 0.001640
## pain 0.1648 0.098904 0.1217129 0.08539 0.08005 0.08309 0.489504
## scared 0.1659 0.098919 0.0324377 0.01673 0.09292 0.47091 0.127911
## think 0.1169 0.274654 0.1546800 0.28427 0.10178 0.01485 0.011694## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.097 1.126 0.6341 0.5689 0.5215 0.4198 0.4004
## Proportion of Variance 0.628 0.181 0.0574 0.0462 0.0389 0.0252 0.0229
## Cumulative Proportion 0.628 0.809 0.8668 0.9131 0.9519 0.9771 1.0000
##
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group, data = pca)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.725 -0.623 -0.434 0.422 2.422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.75241 0.03196 54.84 < 0.0000000000000002 ***
## PC1 0.08502 0.00676 12.57 < 0.0000000000000002 ***
## PC2 0.07760 0.01277 6.08 0.0000000013 ***
## robot.grouphuman-like 0.81115 0.04481 18.10 < 0.0000000000000002 ***
## robot.grouprobotic -0.04475 0.03715 -1.20 0.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.922 on 4255 degrees of freedom
## Multiple R-squared: 0.164, Adjusted R-squared: 0.163
## F-statistic: 208 on 4 and 4255 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group, data = pca)
##
## Standardized Coefficients::
## (Intercept) PC1 PC2
## 0.00000 0.17696 0.08676
## robot.grouphuman-like robot.grouprobotic
## 0.32209 -0.02176##
## Pearson's product-moment correlation
##
## data: pca$PC1 and pca$PC2
## t = -0.0000000000013, df = 4300, p-value = 1
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03003 0.03003
## sample estimates:
## cor
## -0.00000000000001979##
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group + Gender + Age, data = pca)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.650 -0.651 -0.343 0.476 2.567
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.43476 0.17423 8.23
## PC1 0.08064 0.00679 11.88
## PC2 0.07246 0.01267 5.72
## robot.grouphuman-like 0.81046 0.04435 18.27
## robot.grouprobotic -0.04871 0.03677 -1.32
## GenderI prefer not to answer this question 0.11889 0.21107 0.56
## GenderMan 0.45854 0.16796 2.73
## GenderWoman 0.67248 0.16831 4.00
## Age -0.00603 0.00135 -4.49
## Pr(>|t|)
## (Intercept) 0.00000000000000024 ***
## PC1 < 0.0000000000000002 ***
## PC2 0.00000001159271421 ***
## robot.grouphuman-like < 0.0000000000000002 ***
## robot.grouprobotic 0.1854
## GenderI prefer not to answer this question 0.5733
## GenderMan 0.0064 **
## GenderWoman 0.00006567057731519 ***
## Age 0.00000744440065800 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.912 on 4251 degrees of freedom
## Multiple R-squared: 0.182, Adjusted R-squared: 0.18
## F-statistic: 118 on 8 and 4251 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group + Gender + Age, data = pca)
##
## Standardized Coefficients::
## (Intercept)
## 0.00000
## PC1
## 0.16784
## PC2
## 0.08101
## robot.grouphuman-like
## 0.32182
## robot.grouprobotic
## -0.02369
## GenderI prefer not to answer this question
## 0.01271
## GenderMan
## 0.22576
## GenderWoman
## 0.32925
## Age
## -0.06325K-means clustering of a data-driven components
## [1] 3672Distribution of robots among k-means clusters (PCA)
##
## 1 2 3
## actroid 1 0 0
## sofia 1 0 0
## festo 0 1 0
## kb 0 1 0
## kf 0 1 0
## nao 0 1 0
## pepper 0 1 0
## tapia 0 1 0
## atlas 0 0 1
## spot 0 0 1##
## Call:
## lm(formula = uv ~ cluster, data = km)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.618 -0.762 -0.262 0.382 2.238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6183 0.0724 36.17 < 0.0000000000000002 ***
## cluster2 -0.8561 0.0748 -11.44 < 0.0000000000000002 ***
## cluster3 -0.5805 0.0772 -7.52 0.000000000000065 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.987 on 4257 degrees of freedom
## Multiple R-squared: 0.0399, Adjusted R-squared: 0.0395
## F-statistic: 88.6 on 2 and 4257 DF, p-value: <0.0000000000000002##
## Call:
## lm(formula = uv ~ cluster, data = km)
##
## Standardized Coefficients::
## (Intercept) cluster2 cluster3
## 0.0000 -0.4089 -0.2689Distribution of participants among k-means clusters (PCA)
| 1 | 2 | 3 |
|---|---|---|
| 231 | 181 | 14 |
| 0 | 1 | 2 | 3 |
|---|---|---|---|
| 195 | 9 | 174 | 48 |
| 0 | 1 & 2 | 1 & 3 | 2 & 3 |
|---|---|---|---|
| 245 | 3 | 8 | 170 |
